Scientific.Statistics.Histogram.Histogram

Package Scientific :: Package Statistics :: Module Histogram :: Class Histogram |
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# Class Histogram

Known Subclasses:
  - WeightedHistogram

Histogram in one variable

The bin index and the number of points in a bin can be obtained by indexing the histogram with the bin number. Application of len() yields the number of bins. A histogram thus behaves like a sequence of bin index - bin count pairs.

Here is an example on usage:

    >>> nsamples = 1000
    >>> from numpy import random
    >>> data = random.normal(1.0, 0.5, nsamples)
    >>> import Scientific.Statistics as S
    >>> S.mean(data)
    0.9607056871982641
    >>> S.standardDeviation(data)
    0.50251811830486681
    >>> S.median(data)
    0.94853870756924152
    >>> S.skewness(data)   # should be 0
    0.038940041870334556
    >>> S.kurtosis(data)   # should be 3
    2.865582791273765
    >>>
    >>> from Scientific.Statistics.Histogram import Histogram
    >>> h = Histogram(data, 50)  # use 50 bins between min & max samples
    >>> h.normalizeArea()        # make probabilities in histogram
    >>> h[3]                     # bin index and frequency in the 4th bin
    array([-0.45791018,  0.01553658])
    >>> x = h.getBinIndices()
    >>> y = h.getBinCounts()
    >>> # can plot the y vector against the x vector (see below)
    >>>
    >>> # add many more samples:
    >>> nsamples2 = nsamples*100
    >>> data = random.normal(1.0, 0.5, nsamples2)
    >>> h.addData(data)
    >>> h.normalizeArea()
    >>> x2 = h.getBinIndices()
    >>> y2 = h.getBinCounts()

    >>> plot (x,y) and (x2,y2):
    >>> import Gnuplot
    >>> g = Gnuplot.Gnuplot(persist=1)
    >>> g.xlabel('sample value');  g.ylabel('probability')
    >>> d1 = Gnuplot.Data(x, y, with='lines',
    ...                   title='%d samples' % nsamples)
    >>> d2 = Gnuplot.Data(x2, y2, with='lines',
    ...                   title='%d samples' % nsamples2)
    >>> g.plot(d1,d2)

Instance Methods
__getitem__(self, index) Returns: an array of shape (2,) containing the bin value and the bin count |
__getslice__(self, first, last) |
__init__(self, data, nbins, range=None) |
`int` |
__len__(self) Returns: the number of bins |
addData(self, data) Add values to the originally supplied data sequence. |
getBinCounts(self) Return an array of all the bin counts. |
getBinIndices(self) Return an array of all the bin indices. |
normalize(self, norm=1.0) Scale all bin counts by the same factor |
normalizeArea(self, norm=1.0) Scale all bin counts by the same factor |

Method Details

### __getitem__(self, index) (Indexing operator) |
Parameters: `index` (`int`) - a bin index Returns: an array of shape (2,) containing the bin value and the bin count

### __init__(self, data, nbins, range=None) (Constructor) |
Parameters: `data` (`Numeric.array` of `float` or `int`) - a sequence of data points `nbins` (`int`) - the number of bins into which the data is to be sorted `range` (`tuple` or `NoneType`) - a tuple of two values, specifying the lower and the upper end of the interval spanned by the bins. Any data point outside this interval will be ignored. If no range is given, the smallest and largest data values are used to define the interval.

### __len__(self) (Length operator) |
Returns: `int` the number of bins

### addData(self, data) |
Add values to the originally supplied data sequence. Use this method to feed long data sequences in multiple parts to avoid memory shortages. Parameters: `data` (`Numeric.array`) - a sequence of data points Note: this does not affect the default range of the histogram, which is fixed when the histogram is created.

### normalize(self, norm=1.0) |
Scale all bin counts by the same factor Parameters: `norm` (`float` or `int`) - the sum of all bin counts after the rescaling

### normalizeArea(self, norm=1.0) |
Scale all bin counts by the same factor Parameters: `norm` (`float` or `int`) - the area under the histogram after the rescaling